The request's input plus requested output exceeded the selected model's context capacity.
Why does this error happen?
- Conversation history, retrieved documents, tool definitions, or max output made the total token budget too large.
- The effective account, plan, runtime, model, region, or deployment scope may be more restrictive than a headline platform maximum.
- Retries or parallel workers can amplify the condition when they are not bounded or coordinated.
How do you diagnose it?
- Record the exact model ID and count every message, tool schema, attachment, and reserved output token.
- Correlate the provider request identifier and UTC timestamp with application logs and metrics without recording secrets.
- Reproduce with the smallest safe request and verify the exact account, plan, endpoint, region, runtime, or model involved.
How do you fix it?
- Remove irrelevant context, summarize or chunk retrieved data, and reserve a realistic output budget.
- Retry only when the documented error is temporary; use bounded attempts, jitter, idempotency, and a dead-letter path.
- Verify recovery with a controlled request, then monitor the same limiter or failure signal under normal traffic.
How do you prevent it from recurring?
Turn the confirmed cause of context_length_exceeded into an observable boundary for OpenAI API. Track the relevant request count, token volume, payload size, execution time, connection pressure, billing state, or upstream health before it reaches the documented failure condition. Preserve the platform request ID and timestamp so future incidents can be correlated without logging sensitive payloads.
Test the fix under representative concurrency and failure injection, not only with one successful request. Alert on remaining headroom and repeated retries, and keep the linked limit page and official error source with the runbook so responders can distinguish a configuration problem from temporary service pressure or account state.